@inproceedings{swarnkar-singh-2018-di,
title = "Di-{LSTM} Contrast : A Deep Neural Network for Metaphor Detection",
author = "Swarnkar, Krishnkant and
Singh, Anil Kumar",
booktitle = "Proceedings of the Workshop on Figurative Language Processing",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0914",
doi = "10.18653/v1/W18-0914",
pages = "115--120",
abstract = "The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="swarnkar-singh-2018-di">
<titleInfo>
<title>Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Krishnkant</namePart>
<namePart type="family">Swarnkar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anil</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Singh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Figurative Language Processing</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">New Orleans, Louisiana</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.</abstract>
<identifier type="citekey">swarnkar-singh-2018-di</identifier>
<identifier type="doi">10.18653/v1/W18-0914</identifier>
<location>
<url>https://aclanthology.org/W18-0914</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>115</start>
<end>120</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection
%A Swarnkar, Krishnkant
%A Singh, Anil Kumar
%S Proceedings of the Workshop on Figurative Language Processing
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F swarnkar-singh-2018-di
%X The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.
%R 10.18653/v1/W18-0914
%U https://aclanthology.org/W18-0914
%U https://doi.org/10.18653/v1/W18-0914
%P 115-120
Markdown (Informal)
[Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection](https://aclanthology.org/W18-0914) (Swarnkar & Singh, Fig-Lang 2018)
ACL